library(readr)
house_data <- read_csv("Dataset/Rent_House_random_200.csv")
## Rows: 200 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): floor, animal, furniture
## dbl (8): area, rooms, bathroom, parking spaces, hoa, rent amount, property t...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
house_data <- house_data[, c(1:4, 6:11)]
str(house_data)
## tibble [200 × 10] (S3: tbl_df/tbl/data.frame)
## $ area : num [1:200] 120 45 50 35 204 177 15 70 180 180 ...
## $ rooms : num [1:200] 3 1 2 1 4 3 1 2 3 4 ...
## $ bathroom : num [1:200] 4 1 1 1 4 3 1 2 3 4 ...
## $ parking spaces: num [1:200] 3 1 1 0 2 4 0 1 2 2 ...
## $ animal : chr [1:200] "acept" "not acept" "acept" "acept" ...
## $ furniture : chr [1:200] "not furnished" "furnished" "not furnished" "not furnished" ...
## $ hoa : num [1:200] 1350 3000 226 260 0 2700 0 1800 700 2600 ...
## $ rent amount : num [1:200] 5600 5520 750 1400 3440 6900 1200 4200 2700 2000 ...
## $ property tax : num [1:200] 560 0 0 0 100 509 0 250 175 584 ...
## $ fire insurance: num [1:200] 71 70 10 18 62 89 16 55 40 26 ...
# Using prcomp to compute the principal components (eigenvalues and eigenvectors). With scale=TRUE, variable means are set to zero, and variances set to one
rent_pca <- prcomp(house_data[,-c(5,6,8)],scale=TRUE)
rent_pca
## Standard deviations (1, .., p=7):
## [1] 2.1011414 0.9226665 0.7787804 0.6504630 0.5857012 0.4749530 0.3683260
##
## Rotation (n x k) = (7 x 7):
## PC1 PC2 PC3 PC4 PC5
## area 0.4336965 0.17091900 0.11991336 -0.14262889 -0.35241854
## rooms 0.3704037 0.38742007 0.06788605 0.73084114 0.02399273
## bathroom 0.4227960 0.15626819 0.06607019 0.09708057 0.28600555
## parking spaces 0.3939576 0.08267643 0.27820446 -0.52057449 0.56700711
## hoa 0.3144122 -0.55087277 -0.64250123 0.17481916 0.31296716
## property tax 0.2933799 -0.68199336 0.54402406 0.12426281 -0.30542261
## fire insurance 0.3947438 0.14443506 -0.43643932 -0.34511274 -0.52980928
## PC6 PC7
## area -0.04502695 0.78850847
## rooms 0.39473933 -0.13256958
## bathroom -0.81830269 -0.17780729
## parking spaces 0.39883150 -0.09488344
## hoa 0.08754800 0.22068420
## property tax 0.01872523 -0.20922784
## fire insurance 0.07383965 -0.47705723
summary(rent_pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.1011 0.9227 0.77878 0.65046 0.58570 0.47495 0.36833
## Proportion of Variance 0.6307 0.1216 0.08664 0.06044 0.04901 0.03223 0.01938
## Cumulative Proportion 0.6307 0.7523 0.83894 0.89939 0.94839 0.98062 1.00000
(eigen_rent <- rent_pca$sdev^2)
## [1] 4.4147952 0.8513135 0.6064989 0.4231021 0.3430459 0.2255804 0.1356640
names(eigen_rent) <- paste("PC",1:7,sep="")
eigen_rent
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 4.4147952 0.8513135 0.6064989 0.4231021 0.3430459 0.2255804 0.1356640
sumlambdas <- sum(eigen_rent)
sumlambdas
## [1] 7
propvar <- eigen_rent/sumlambdas
propvar
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 0.63068502 0.12161621 0.08664270 0.06044316 0.04900656 0.03222577 0.01938057
cumvar_rent <- cumsum(propvar)
cumvar_rent
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## 0.6306850 0.7523012 0.8389439 0.8993871 0.9483937 0.9806194 1.0000000
matlambdas <- rbind(eigen_rent,propvar,cumvar_rent)
rownames(matlambdas) <- c("Eigenvalues","Prop. variance","Cum. prop. variance")
round(matlambdas,4)
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalues 4.4148 0.8513 0.6065 0.4231 0.3430 0.2256 0.1357
## Prop. variance 0.6307 0.1216 0.0866 0.0604 0.0490 0.0322 0.0194
## Cum. prop. variance 0.6307 0.7523 0.8389 0.8994 0.9484 0.9806 1.0000
summary(rent_pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.1011 0.9227 0.77878 0.65046 0.58570 0.47495 0.36833
## Proportion of Variance 0.6307 0.1216 0.08664 0.06044 0.04901 0.03223 0.01938
## Cumulative Proportion 0.6307 0.7523 0.83894 0.89939 0.94839 0.98062 1.00000
rent_pca$rotation
## PC1 PC2 PC3 PC4 PC5
## area 0.4336965 0.17091900 0.11991336 -0.14262889 -0.35241854
## rooms 0.3704037 0.38742007 0.06788605 0.73084114 0.02399273
## bathroom 0.4227960 0.15626819 0.06607019 0.09708057 0.28600555
## parking spaces 0.3939576 0.08267643 0.27820446 -0.52057449 0.56700711
## hoa 0.3144122 -0.55087277 -0.64250123 0.17481916 0.31296716
## property tax 0.2933799 -0.68199336 0.54402406 0.12426281 -0.30542261
## fire insurance 0.3947438 0.14443506 -0.43643932 -0.34511274 -0.52980928
## PC6 PC7
## area -0.04502695 0.78850847
## rooms 0.39473933 -0.13256958
## bathroom -0.81830269 -0.17780729
## parking spaces 0.39883150 -0.09488344
## hoa 0.08754800 0.22068420
## property tax 0.01872523 -0.20922784
## fire insurance 0.07383965 -0.47705723
print(rent_pca)
## Standard deviations (1, .., p=7):
## [1] 2.1011414 0.9226665 0.7787804 0.6504630 0.5857012 0.4749530 0.3683260
##
## Rotation (n x k) = (7 x 7):
## PC1 PC2 PC3 PC4 PC5
## area 0.4336965 0.17091900 0.11991336 -0.14262889 -0.35241854
## rooms 0.3704037 0.38742007 0.06788605 0.73084114 0.02399273
## bathroom 0.4227960 0.15626819 0.06607019 0.09708057 0.28600555
## parking spaces 0.3939576 0.08267643 0.27820446 -0.52057449 0.56700711
## hoa 0.3144122 -0.55087277 -0.64250123 0.17481916 0.31296716
## property tax 0.2933799 -0.68199336 0.54402406 0.12426281 -0.30542261
## fire insurance 0.3947438 0.14443506 -0.43643932 -0.34511274 -0.52980928
## PC6 PC7
## area -0.04502695 0.78850847
## rooms 0.39473933 -0.13256958
## bathroom -0.81830269 -0.17780729
## parking spaces 0.39883150 -0.09488344
## hoa 0.08754800 0.22068420
## property tax 0.01872523 -0.20922784
## fire insurance 0.07383965 -0.47705723
house_data$rent <- ifelse(house_data$`rent amount` > 3891, "high", "low")
# Identifying the scores by their rent status
rent_amount_pca <- cbind(data.frame(house_data$`rent`),rent_pca$x)
rent_amount_pca
## house_data.rent PC1 PC2 PC3 PC4
## 1 high 1.56783825 0.216106460 0.1006524694 -0.121661131
## 2 high -0.57639521 -1.415621716 -1.8450487308 -0.568054942
## 3 low -1.50346548 0.047393945 0.3146626557 0.075959005
## 4 low -2.07514390 -0.353409399 -0.0487786567 -0.213547579
## 5 low 1.34530122 1.543191001 0.6261747058 0.566419730
## 6 high 2.24975917 -0.304496824 -0.6359827011 -0.569522889
## 7 low -2.23846999 -0.266490242 0.0926178033 -0.212726044
## 8 high -0.22912144 -0.584847508 -0.7695133831 0.056490473
## 9 low 0.64873235 0.609778758 0.3561100132 0.182018436
## 10 low 1.79476843 -0.172057539 -0.2280405746 1.316321010
## 11 low -0.95873902 -0.028834196 0.1474446455 0.170387563
## 12 high 2.13362330 -0.340120927 0.3471215144 -0.028438097
## 13 low -1.01660046 0.184564734 0.2334791298 0.244747393
## 14 low -2.07539668 -0.503254179 -0.1877055263 -0.142237669
## 15 low -1.35466295 0.014694313 0.2082497129 0.034576983
## 16 low -0.76938889 0.177445816 -0.0117026192 1.164270693
## 17 high -0.82924339 -0.283533687 -0.3362267145 -0.808440700
## 18 low -1.67879982 -0.732653505 -0.4699507755 -0.195224545
## 19 high 0.63656545 0.254965890 -0.3726995379 0.470457449
## 20 low -2.03744269 -0.436072675 -0.0208544152 -0.173039886
## 21 low -1.54280272 0.162255561 0.4101846230 0.020917191
## 22 high 0.16777452 0.541686279 0.0862675807 0.016597064
## 23 high 3.19501820 1.625800103 1.2208364392 -0.437836947
## 24 high 3.50284784 2.828627579 -0.4270439877 3.021320433
## 25 low -0.32699280 0.668985737 0.3654347279 0.148866006
## 26 low -1.89708178 -0.561270151 -0.2264566344 -0.146433688
## 27 low -2.05561487 -0.358852444 -0.0833815781 -0.235174184
## 28 low -1.89149015 -0.180148663 0.3430894004 -0.588317122
## 29 low -0.87692112 -0.111563708 0.1528911503 0.165100917
## 30 high 1.85262375 1.096409200 0.3129228017 -0.494026791
## 31 low -1.88522590 -0.535453098 -0.3545206600 -0.203286079
## 32 high 1.13964853 -0.038181296 -0.2171061084 0.849371034
## 33 high 1.30826933 0.088697159 0.2887956702 -0.145902126
## 34 low -1.22973070 0.051707352 0.1518058641 -0.023532342
## 35 low 2.24230187 0.498753274 0.2538800688 0.748496378
## 36 high 1.94733013 -0.376827121 0.4992531656 0.436069290
## 37 high 8.72831120 -4.001864821 -1.7044282832 0.278709593
## 38 low -0.61880179 0.702893459 0.5867092275 0.613271626
## 39 low -2.02535867 -0.502475425 -0.2403781125 -0.169854715
## 40 low 0.07631034 0.738460865 0.2501213215 1.207803582
## 41 low -2.08583746 -0.234423820 0.1273052975 -0.264569312
## 42 high 2.94958392 0.427892318 -0.2277217426 -0.057963432
## 43 low 0.68623055 0.197753792 0.0728335361 0.303568344
## 44 low 0.27919326 0.633781791 0.2892257520 0.194552423
## 45 low 1.10879850 0.272393159 -0.1253531306 1.284610499
## 46 high 2.05071676 1.472483035 1.1316154236 -1.112095378
## 47 high 4.21959089 0.100950841 -0.9469743675 -0.648501558
## 48 low -1.27087429 0.301136038 0.0872555875 0.989808541
## 49 high 4.58113442 -0.081008408 -3.0702617795 -1.484704799
## 50 low -2.02223866 -0.307741537 -0.0026440759 -0.247242514
## 51 low -0.42267359 0.242911565 0.0287822969 0.727401222
## 52 low -0.90365748 0.104475522 0.1721109085 0.035788515
## 53 low -0.58003387 0.329728014 0.2223298923 0.705890676
## 54 high 2.90035484 -1.570597144 0.1787300097 0.048382923
## 55 low 6.70167586 -7.240368801 5.7122500453 1.569939729
## 56 low -1.58788243 -0.152880120 -0.1212449872 0.445355642
## 57 low -2.08095556 -0.361259848 -0.0448878179 -0.204601143
## 58 low -2.07987411 -0.204708260 0.1011651456 -0.278814209
## 59 low -1.23803192 -0.239285062 0.1066257807 0.127084933
## 60 low -1.27346638 0.227541583 0.2940400969 -0.141133413
## 61 low -2.06371912 -0.398381045 0.0192251237 -0.166788952
## 62 low 0.37233762 -0.654972412 0.0250325045 -0.125326341
## 63 high 0.16309312 0.463109560 -0.5870658673 0.724792365
## 64 low -1.99481328 -0.230902340 0.0164564910 -0.346782693
## 65 high 1.30865679 1.223530239 -0.2185786966 -0.673072950
## 66 low -0.39000716 0.515254147 0.3673203710 0.577268698
## 67 low 0.64357804 0.421824081 0.0884939598 0.294100301
## 68 low -1.72324081 -0.400853043 0.1446466693 -0.547683814
## 69 low -2.01219554 -0.452081716 -0.1779667165 -0.222038946
## 70 low -2.11306394 -0.376864287 0.0432032659 -0.155543996
## 71 low -1.60270788 -0.107661056 0.0978338074 0.421153939
## 72 low 0.77543632 0.840639563 0.8125250938 -0.213977712
## 73 low 0.52578996 1.201643439 0.5722349371 0.848863034
## 74 low -2.19000223 -0.275806650 0.1225112186 -0.220800775
## 75 high 0.28960911 0.175849250 -0.0345351070 0.145994843
## 76 low -1.76754748 -0.173722835 0.0623678331 -0.426690608
## 77 low -1.21365160 0.162691257 0.3434054469 0.143526752
## 78 low -1.80275583 -0.328698732 0.2454104797 -0.553728334
## 79 low -1.75325429 -0.360381071 -0.3307310625 -0.382124982
## 80 low 0.80748929 0.472345300 -0.1040745716 0.796495848
## 81 low -0.80315118 0.691862669 0.3270645972 0.913433400
## 82 low -1.13014395 0.104196186 0.3472714236 0.147257619
## 83 low 0.19883740 0.908240616 0.8573177480 -0.282468335
## 84 low -0.85903456 0.011756540 0.9862872872 -1.751171429
## 85 low -0.30037935 0.591144964 0.6741041657 0.263472446
## 86 low -1.47075763 0.098284736 0.2957358096 0.021078241
## 87 high -0.22483293 0.395265595 -0.2209099434 -1.672353342
## 88 low -2.28955542 -0.285182186 0.1490992353 -0.168063565
## 89 low -0.41144395 0.625718703 0.2050961194 0.149367786
## 90 high 1.51846473 0.279585178 -0.4708986879 0.795976786
## 91 low 0.89449869 1.117748417 0.3304967504 0.553363763
## 92 low -1.53674605 -0.100957919 0.3801463564 -0.210659210
## 93 high 6.19640421 -0.110241455 3.3822304210 -2.805589326
## 94 low -1.86005657 -0.474529161 -0.2076998800 -0.273596003
## 95 low 0.33212522 0.226532434 -0.1097068019 0.694394101
## 96 low 0.80830540 0.838485361 0.6027470151 0.015415555
## 97 low 1.13265608 -0.765402951 -0.0569401156 -0.641086478
## 98 low -0.91478591 0.051041051 0.1060233162 0.077531582
## 99 low -1.03005630 0.069827300 0.2207939449 0.128116383
## 100 low -0.22835744 -1.993479512 -2.3805418101 0.223876458
## 101 high 4.55935064 -1.051257329 -2.0964459441 1.006330448
## 102 low -1.78927235 -0.679650433 -0.4539693735 -0.171252851
## 103 low -1.46833723 -0.425998182 0.1282182677 0.503537374
## 104 low -1.86780325 -0.411382074 -0.1654655143 -0.233627064
## 105 low -1.58158590 -0.406008908 0.0230445862 -0.618203957
## 106 high 3.40634597 -0.323061949 -0.4452667611 0.240925817
## 107 high 2.81079495 0.147142954 -1.3679600097 -0.508256718
## 108 low -1.76194430 0.021059052 0.1047786588 0.378895520
## 109 low -1.95834325 -0.612496796 -0.2284980060 -0.164177316
## 110 low -1.47171259 -0.073388736 -0.0185860717 0.350698744
## 111 low -2.01386835 -0.403640779 -0.1062979106 -0.220561376
## 112 high 5.76211882 -1.137833269 0.3216977116 -0.247423151
## 113 low -1.71655905 -0.452427504 0.0243278629 -0.545070856
## 114 low -1.36509520 -0.378424410 -0.0797508969 -0.714234029
## 115 low -0.65189695 -0.113236592 -0.1763077052 0.514064482
## 116 low 0.13935881 -0.071811510 -0.3566526532 0.629810148
## 117 high 1.93908906 -0.749329254 -1.0016189606 -0.161833200
## 118 low -0.36387251 0.327039239 -0.0640485327 0.657436626
## 119 high 3.06217521 -0.102970811 -0.8247131126 -0.357370907
## 120 low 0.21859561 0.037872853 -0.1246301221 0.284608250
## 121 low -1.76243407 0.010817983 0.1048273494 0.416388145
## 122 high -0.43837393 -0.240555129 -0.6768066510 -1.063240405
## 123 low -1.02785008 0.128444776 0.0674839263 -0.191208139
## 124 low -2.09994961 -0.213340349 0.0603113810 -0.286143074
## 125 low -2.05366442 -0.454152520 0.0653452736 -0.154271356
## 126 low -2.13691077 -0.322228158 0.1995362903 -0.187430663
## 127 low -1.79886574 0.064560778 0.2479554409 0.393797437
## 128 low -0.73346935 0.412906610 -0.0015018796 0.463288506
## 129 low -0.52651398 0.383270016 0.1459561546 0.668166167
## 130 low -1.76290055 -0.461315238 0.1287221991 -0.474252553
## 131 low -2.19205769 -0.288075246 0.1643141751 -0.202823134
## 132 low -1.60079651 0.154981538 0.2241031268 0.296976666
## 133 low -0.94453039 0.304808873 0.1088423433 0.623599796
## 134 high -0.57639521 -1.415621716 -1.8450487308 -0.568054942
## 135 high 4.53213015 -0.402724703 -1.9711126384 -0.192299028
## 136 high -1.20831130 -0.490355876 -0.3674765904 -0.813156009
## 137 low -1.99402495 -0.438325527 -0.1996207950 -0.239338848
## 138 low -1.74622046 -0.258226538 0.1295033127 -0.101829169
## 139 low -0.65198241 -0.041097747 -0.0720391714 -0.021595749
## 140 high 2.05137269 0.095572333 0.0061182915 0.405188976
## 141 high 0.87380363 0.576758767 0.0688528052 0.713976175
## 142 low -2.12397932 -0.245487379 0.1501776037 -0.238993450
## 143 low 0.96227605 1.034485851 0.7930384412 0.071989745
## 144 low -1.01666875 0.081091724 0.2266674509 0.087505628
## 145 high 1.57023222 0.255435229 -0.3052585402 0.214625907
## 146 low -0.04858875 0.830737518 0.4734066649 -0.001857213
## 147 high 3.68932809 2.143094773 0.7070134257 -0.318375593
## 148 low -1.91380925 -0.341069088 -0.0135300355 -0.226507296
## 149 low -0.56676741 0.197178457 0.2227275227 -0.391935961
## 150 low -2.21839448 -0.257858154 0.1334715678 -0.205397179
## 151 high -1.28894407 -0.557821845 -0.4672134385 -0.734662855
## 152 high -0.15718144 0.594554690 0.4669112596 -1.208489344
## 153 low -1.38189083 0.055106991 0.2532240009 0.011308292
## 154 low -0.47778490 0.278574381 0.3785521091 -0.509419539
## 155 low -2.14612555 -0.268589740 0.1643039031 -0.223021844
## 156 high 2.62293011 0.046505699 -1.9347451634 -0.793511580
## 157 low -0.31225768 0.644030033 0.4338849246 0.216917926
## 158 high 3.76474618 0.110565101 -1.2699536885 0.152972777
## 159 high 0.85651631 0.294530133 -0.9211415805 0.389242523
## 160 high 2.11184808 2.050942284 0.5521557089 1.261666456
## 161 low -2.15956186 -0.389180550 0.0104111649 -0.157618095
## 162 low -1.34879407 -0.009258708 0.1083837634 0.015350633
## 163 low -0.60762535 0.355905635 0.2887305717 0.731005641
## 164 high 2.02586190 0.173151622 -0.8155604445 -0.287006039
## 165 low -1.05921271 -1.257469439 -0.6819339681 -0.446180451
## 166 low -1.03323781 -0.198548314 -0.2861963706 -0.009953744
## 167 low -0.10756112 0.493888032 0.5229805929 0.233120262
## 168 high 3.97320720 1.661466488 1.3303842900 -0.207147700
## 169 high 6.40500435 2.156126384 1.5470106528 -2.330872365
## 170 low -1.56351179 -0.478888242 -0.0008992945 -0.567448765
## 171 high 4.70912592 1.959217051 0.8330720923 -1.051735715
## 172 low 0.91652794 -0.157807836 0.0695329636 -0.663844743
## 173 high 3.01780491 -3.781663903 0.8357036000 0.868639547
## 174 low -0.14094557 0.067412311 -0.1700131624 0.659285684
## 175 low -1.95900629 -0.206996774 0.3126540808 -0.570756911
## 176 high 1.89765204 1.621983337 -0.3189579339 -0.978750094
## 177 low -2.16356341 -0.298112009 0.0357264063 -0.216893534
## 178 low -2.04455537 -0.454658134 -0.1167689509 -0.157920212
## 179 low -0.08406563 0.339842914 -0.0294969343 0.486867554
## 180 low -1.87051599 -0.556010555 -0.1849082846 -0.150590752
## 181 low -0.15840553 0.298411747 -0.3890680629 1.023969590
## 182 high 2.57672105 0.835200666 -0.1981408254 0.588203116
## 183 high 1.93043286 1.468098200 1.3121416688 -0.569814917
## 184 low -0.21811819 -0.062551593 -0.1622152608 0.782733201
## 185 low -1.66127191 -0.157066582 0.0230435231 -0.487630521
## 186 low -1.84040471 -0.161456719 0.2866079684 -0.632979600
## 187 high 4.40504874 0.108932928 -1.3397219418 -0.652162530
## 188 low 0.27881638 0.134381161 0.0187600095 -0.380996609
## 189 low -0.16493909 0.509384595 -0.2625570830 1.671940700
## 190 low -0.50611546 -0.040305850 0.3322741409 -0.289280669
## 191 high 0.65734764 0.157810165 -0.2833824679 0.559127745
## 192 high 0.96346403 0.745828560 -0.5786646081 0.847574043
## 193 high 3.16873531 0.056330941 -1.2876187644 -0.090831208
## 194 high 1.58293083 0.159448603 -0.1721118343 -0.350725312
## 195 low -1.43496833 0.162715787 0.3929544973 -0.031172825
## 196 low -1.41999697 -0.001964711 0.2589000227 0.075919164
## 197 high 5.01064356 -0.837240083 -1.2685998359 -0.107233999
## 198 low -0.49933014 0.370414441 0.3463092433 0.713629506
## 199 high -1.33180077 0.285777235 -0.1098022854 0.046297098
## 200 low -1.21380272 -0.144475181 -0.0122758957 0.022187477
## PC5 PC6 PC7
## 1 0.8826685806 -0.37155494 -0.652309477
## 2 0.2647664146 0.36757198 0.028219570
## 3 0.2090874646 0.39642134 0.041396776
## 4 -0.2465915926 -0.19841094 0.041238497
## 5 0.1268736556 -0.47518491 -0.182963676
## 6 1.0661267537 0.62606338 -0.110920625
## 7 -0.2318992105 -0.21326856 -0.125734057
## 8 0.1919455610 -0.02649857 -0.170601123
## 9 0.3838699556 -0.17186887 0.239420212
## 10 1.1613661658 -0.31816341 0.411704385
## 11 0.4262656529 -0.18383886 0.087079082
## 12 0.6261856288 -0.36662189 -0.088591731
## 13 -0.5975235893 -0.48133022 -0.068057089
## 14 -0.1108141793 -0.17658010 0.014186128
## 15 0.1255709986 0.41042693 0.105556705
## 16 0.0106408429 -0.13154428 0.292917274
## 17 -0.1922592484 -0.43330274 -0.389826301
## 18 -0.2779252956 -0.12803014 0.129572181
## 19 -0.1307831555 -0.34796916 -0.232051718
## 20 -0.2191044191 -0.20007967 0.142622182
## 21 0.1170964147 0.38458669 -0.039254413
## 22 0.1308195853 0.49166451 -0.423574239
## 23 0.1360870018 -0.50326397 -0.087159083
## 24 -2.0024309975 0.44926000 -0.653456230
## 25 0.4351206934 0.44997384 -0.630877417
## 26 -0.1647398582 -0.17741818 0.287627604
## 27 -0.2815196013 -0.18978103 -0.037672808
## 28 0.1245282994 0.05300405 0.011093252
## 29 0.3635219262 -0.17448084 0.064066335
## 30 -0.8761619265 -0.71570141 -0.571179831
## 31 -0.2174839032 -0.15533492 0.073410553
## 32 0.2106339194 0.91449173 -0.097836840
## 33 0.6530800637 0.19923080 -0.129166151
## 34 0.0220761334 0.41322082 0.203122550
## 35 1.3711438884 -0.67395345 -0.282508215
## 36 1.6133906820 -1.02080069 0.153092432
## 37 -0.5935284150 -0.39561704 1.567565032
## 38 0.1006836631 0.08545129 0.180486246
## 39 -0.1543627260 -0.16866600 -0.007268863
## 40 0.0295829676 0.48586158 0.107011958
## 41 -0.3672647161 -0.22245958 0.074527502
## 42 0.9230689466 0.31233732 -0.350275555
## 43 0.5637742368 -0.10336173 -0.098328893
## 44 0.5236697654 -0.13869356 -0.564005262
## 45 0.0365471459 -0.04026277 0.295613153
## 46 0.0451082285 0.28839711 1.249763991
## 47 -0.3487199230 0.49152609 -1.077787696
## 48 -0.3266469601 0.46463479 0.056246409
## 49 -0.0565338325 0.31815913 -0.355696246
## 50 -0.3242271864 -0.20702495 0.120685231
## 51 0.3223890632 0.17600324 0.037461623
## 52 0.1937016765 -0.17886050 -0.126102949
## 53 0.2712731924 0.16211897 -0.218563103
## 54 0.0806018599 0.32607158 0.758849158
## 55 -0.7414408847 0.13034774 -1.103420929
## 56 -0.1869680993 0.14648102 0.217575546
## 57 -0.2324739566 -0.19925106 0.053425088
## 58 -0.3742996372 -0.22230437 0.085302417
## 59 0.2218358453 0.43189101 0.228480862
## 60 -0.1859940546 0.40345581 -0.047439743
## 61 -0.2022809065 -0.21046918 0.206448167
## 62 0.7076170094 -0.39211112 -0.285823917
## 63 -0.5955976030 -0.62831523 -0.573206379
## 64 -0.5023016837 -0.19791150 -0.122198137
## 65 -0.9611509766 -0.05590851 -1.029557223
## 66 -0.0057205691 0.13109536 0.077218724
## 67 0.6366932422 -0.14178246 0.291938021
## 68 0.1597990979 0.08696266 0.066090177
## 69 -0.2643086468 -0.17064303 -0.104731833
## 70 -0.1707213827 -0.21542999 0.196668739
## 71 -0.3004831656 0.12552337 0.204117066
## 72 -0.6217472283 0.37359587 0.760916594
## 73 -0.7148404521 0.42534250 0.587692704
## 74 -0.2736716973 -0.21654820 -0.064072602
## 75 0.2616471875 0.52569153 -0.375791814
## 76 -0.7031636421 -0.21212410 0.184205785
## 77 0.4259891220 -0.20756041 -0.183859506
## 78 0.1534207997 0.07276770 0.029640655
## 79 -0.5363159305 -0.15307912 -0.070950246
## 80 0.3350190034 0.85107024 0.066111114
## 81 -0.4049081907 -0.18855040 0.247600489
## 82 0.3907530118 -0.20552794 -0.099259487
## 83 0.6084468182 0.66276882 0.076478202
## 84 1.1414814164 0.88617702 0.040220101
## 85 0.5003348026 0.40095970 -0.128266231
## 86 0.1222295170 0.40086000 -0.033240314
## 87 -0.5595259481 -0.15469603 -0.657858098
## 88 -0.1633343828 -0.22282445 -0.063996092
## 89 -0.6158188116 -1.12693908 0.445965970
## 90 0.3639677031 0.32356598 -0.266883056
## 91 -0.0776519985 0.74583563 0.689480247
## 92 -0.3146249830 -0.87171661 0.501719501
## 93 -0.5280773831 0.34146586 0.078342898
## 94 -0.3949240309 -0.16320876 -0.046207510
## 95 0.2571860166 -0.40213547 0.112379017
## 96 1.7003208877 -2.94676390 -1.058594839
## 97 1.9873942851 0.17642603 0.554999369
## 98 0.2830213414 -0.17314998 -0.083900817
## 99 0.3685569665 -0.19105077 -0.051250402
## 100 0.6065903130 -0.49340761 0.901746589
## 101 -1.9935215752 -1.14236478 -0.124162554
## 102 -0.1927189112 -0.13621111 0.107202134
## 103 -0.3058031426 0.14842006 0.186191916
## 104 -0.3068224231 -0.19018922 0.294267788
## 105 0.0376694600 0.10603791 0.030466947
## 106 1.2004202260 0.35012604 0.430408936
## 107 0.4162225349 -0.22850894 0.077226416
## 108 -0.3010366787 0.12702189 -0.089672025
## 109 -0.2058064932 -0.15631287 -0.076696294
## 110 -0.4066809765 0.13741590 0.238442933
## 111 -0.2704238006 -0.18705929 0.027015529
## 112 1.8010328037 -0.24670776 0.663535696
## 113 0.1909699764 0.10688884 -0.048195644
## 114 -0.1449982851 0.11714335 0.133927338
## 115 0.0097020496 -1.02861486 -0.164182023
## 116 0.0398066964 0.24786659 0.115511203
## 117 -0.6755187287 0.72768949 -0.231209035
## 118 0.2429353351 0.18269689 0.019915353
## 119 0.4554416363 -0.82946182 -0.623637445
## 120 0.5296724555 0.52810692 -0.203702547
## 121 -0.2315616255 0.11832517 0.072427061
## 122 -0.6225769134 -0.37411712 -0.652858571
## 123 -0.2857181484 0.43491691 0.032780079
## 124 -0.3715130180 -0.21279027 -0.028505579
## 125 -0.2203070845 -0.20843752 0.166967572
## 126 -0.2686720442 -0.22930100 0.134945559
## 127 -0.3037082319 0.10439910 0.021922486
## 128 -0.1616240288 0.78273644 -0.086639766
## 129 0.2374639884 0.16451359 -0.158625092
## 130 0.2945634692 0.08299878 0.187836206
## 131 -0.2594982679 -0.22301363 -0.001840435
## 132 -0.4905511416 0.09784380 0.240268603
## 133 0.1109646141 0.76771233 -0.047374155
## 134 0.2647664146 0.36757198 0.028219570
## 135 -0.5456648668 0.29310706 -0.312381820
## 136 -0.3021092512 0.17927705 -0.291229586
## 137 -0.2880344631 -0.16808015 -0.116885402
## 138 -0.0190312678 -0.82881280 0.109707268
## 139 0.0669934180 -0.13516805 -0.182546755
## 140 0.4538242779 0.60807221 -0.362368641
## 141 0.1316524544 0.83184640 0.096116553
## 142 -0.3224714296 -0.22612973 0.085513662
## 143 0.2077237452 -0.82578562 -0.005185148
## 144 0.2882601370 -0.18344278 -0.193679647
## 145 0.4859300467 -0.66984793 0.155367450
## 146 0.1012382811 0.42496176 -0.290838289
## 147 -0.6741237173 0.50671349 -0.342373105
## 148 -0.3156790988 -0.21576928 0.410490814
## 149 0.5322008381 0.12074107 -0.292627776
## 150 -0.2346858296 -0.22278265 -0.011926061
## 151 -0.1180780318 0.18696636 -0.275829228
## 152 0.0244337343 0.97905957 0.100365173
## 153 0.0839061208 0.40759367 0.026358490
## 154 0.2532018700 0.11546219 -0.440228590
## 155 -0.3011947928 -0.22540589 0.057411714
## 156 -0.0290524597 0.47553138 -0.588827473
## 157 0.5281628656 0.42755390 -0.302341511
## 158 0.2189079037 0.10160772 0.874213970
## 159 -0.1031427198 -0.30212310 -0.088940147
## 160 -0.9120431378 -0.45913493 0.693114623
## 161 -0.1566459981 -0.20493853 0.036319480
## 162 0.1169189470 0.42938274 -0.050300508
## 163 0.3103052412 0.14623935 -0.086914072
## 164 -0.5362151505 0.02600726 0.048180891
## 165 0.2066722487 0.23908267 0.015777024
## 166 0.0819319922 0.48039472 0.082200944
## 167 0.4212675312 0.42439223 -0.063000875
## 168 -0.7276764615 -1.42450764 0.619796432
## 169 -1.7156545419 0.06318158 1.513920149
## 170 0.1139346057 0.10624208 0.149941782
## 171 -0.9921324652 -1.03233531 -0.244789764
## 172 0.9469878124 -0.15889058 0.479326704
## 173 -2.0495450662 0.51790023 -0.330516787
## 174 0.1383527555 0.22150814 -0.063382479
## 175 0.1748825497 0.06249028 -0.137428098
## 176 -1.4886287734 -0.09285025 -0.161477292
## 177 -0.2459708133 -0.20772109 -0.046990106
## 178 -0.1571527212 -0.19114065 0.140501419
## 179 -0.1597784372 0.19916674 -0.107356015
## 180 -0.1930234666 -0.18366772 0.351855031
## 181 -0.0845980597 -0.66973598 -0.232927321
## 182 1.2983503473 -0.68331683 0.478896860
## 183 1.0567421233 0.97292153 -0.200028808
## 184 0.3422127625 0.21814047 0.052602285
## 185 -0.8230630062 -0.20436957 0.175117510
## 186 0.0559634717 0.06255995 -0.050644713
## 187 -0.2362094037 0.51005908 -0.682039560
## 188 0.5413345878 -0.46345163 0.104462940
## 189 -0.1281518971 0.23652308 0.288729074
## 190 0.5828389709 0.12167522 -0.183216436
## 191 -0.0354879214 -0.36472916 0.043996297
## 192 -0.5333889763 0.58054954 0.377026305
## 193 -0.2578763066 0.77227412 -0.391975453
## 194 0.4043906567 0.26099807 -0.381267673
## 195 -0.0009935399 0.39075418 -0.035614077
## 196 0.1938076348 0.40332734 0.119388709
## 197 0.4116819322 -0.05750868 -0.287258782
## 198 0.2359013218 0.13268758 0.137941247
## 199 -0.8546416298 0.14969497 -0.090368227
## 200 0.0959579417 0.45223389 -0.028134034
# Means of scores for all the PC's classified by rent status
tabmeansPC <- aggregate(rent_amount_pca[,2:8],by=list(Rent=house_data$rent),mean)
tabmeansPC
## Rent PC1 PC2 PC3 PC4 PC5 PC6
## 1 high 2.1915308 0.14258453 -0.3020136 -0.18167517 -0.10121578 0.04112931
## 2 low -0.9617509 -0.06257307 0.1325384 0.07972795 0.04441843 -0.01804955
## PC7
## 1 -0.08067297
## 2 0.03540324
tabmeansPC <- tabmeansPC[rev(order(tabmeansPC$Rent)),]
tabmeansPC
## Rent PC1 PC2 PC3 PC4 PC5 PC6
## 2 low -0.9617509 -0.06257307 0.1325384 0.07972795 0.04441843 -0.01804955
## 1 high 2.1915308 0.14258453 -0.3020136 -0.18167517 -0.10121578 0.04112931
## PC7
## 2 0.03540324
## 1 -0.08067297
tabfmeans <- t(tabmeansPC[,-1])
tabfmeans
## 2 1
## PC1 -0.96175094 2.19153084
## PC2 -0.06257307 0.14258453
## PC3 0.13253836 -0.30201363
## PC4 0.07972795 -0.18167517
## PC5 0.04441843 -0.10121578
## PC6 -0.01804955 0.04112931
## PC7 0.03540324 -0.08067297
colnames(tabfmeans) <- t(as.vector(tabmeansPC[1]$Rent))
tabfmeans
## low high
## PC1 -0.96175094 2.19153084
## PC2 -0.06257307 0.14258453
## PC3 0.13253836 -0.30201363
## PC4 0.07972795 -0.18167517
## PC5 0.04441843 -0.10121578
## PC6 -0.01804955 0.04112931
## PC7 0.03540324 -0.08067297
# Standard deviations of scores for all the PC's classified by rent status
tabsdsPC <- aggregate(rent_amount_pca[,2:8],by=list(Rent=house_data$rent),sd)
tabfsds <- t(tabsdsPC[,-1])
colnames(tabfsds) <- t(as.vector(tabsdsPC[1]$Rent))
tabfsds
## high low
## PC1 2.0683291 1.2048325
## PC2 1.1876981 0.7754820
## PC3 1.0436622 0.5856605
## PC4 0.8740072 0.5069594
## PC5 0.8239896 0.4392800
## PC6 0.5552749 0.4360745
## PC7 0.5342930 0.2595674
t.test(PC1~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC1 by house_data$rent
## t = 11.109, df = 78.444, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## 2.588218 3.718345
## sample estimates:
## mean in group high mean in group low
## 2.1915308 -0.9617509
t.test(PC2~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC2 by house_data$rent
## t = 1.2382, df = 83.283, p-value = 0.2191
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.1243662 0.5346814
## sample estimates:
## mean in group high mean in group low
## 0.14258453 -0.06257307
t.test(PC3~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC3 by house_data$rent
## t = -3.0482, df = 77.089, p-value = 0.003153
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.7184235 -0.1506805
## sample estimates:
## mean in group high mean in group low
## -0.3020136 0.1325384
t.test(PC4~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC4 by house_data$rent
## t = -2.1805, df = 78.284, p-value = 0.03222
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.50005662 -0.02274962
## sample estimates:
## mean in group high mean in group low
## -0.18167517 0.07972795
t.test(PC5~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC5 by house_data$rent
## t = -1.3016, df = 75.391, p-value = 0.197
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.36850582 0.07723741
## sample estimates:
## mean in group high mean in group low
## -0.10121578 0.04441843
t.test(PC6~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC6 by house_data$rent
## t = 0.73843, df = 93.884, p-value = 0.4621
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.0999465 0.2183042
## sample estimates:
## mean in group high mean in group low
## 0.04112931 -0.01804955
t.test(PC7~house_data$rent,data=rent_amount_pca)
##
## Welch Two Sample t-test
##
## data: PC7 by house_data$rent
## t = -1.6152, df = 72.733, p-value = 0.1106
## alternative hypothesis: true difference in means between group high and group low is not equal to 0
## 95 percent confidence interval:
## -0.25931118 0.02715875
## sample estimates:
## mean in group high mean in group low
## -0.08067297 0.03540324
## F ratio tests
var.test(PC1~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC1 by house_data$rent
## F = 2.947, num df = 60, denom df = 138, p-value = 1.908e-07
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.949284 4.623972
## sample estimates:
## ratio of variances
## 2.947039
var.test(PC2~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC2 by house_data$rent
## F = 2.3457, num df = 60, denom df = 138, p-value = 4.36e-05
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.551522 3.680424
## sample estimates:
## ratio of variances
## 2.345679
var.test(PC3~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC3 by house_data$rent
## F = 3.1756, num df = 60, denom df = 138, p-value = 2.47e-08
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.100474 4.982615
## sample estimates:
## ratio of variances
## 3.175617
var.test(PC4~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC4 by house_data$rent
## F = 2.9722, num df = 60, denom df = 138, p-value = 1.521e-07
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.965952 4.663509
## sample estimates:
## ratio of variances
## 2.972238
var.test(PC5~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC5 by house_data$rent
## F = 3.5185, num df = 60, denom df = 138, p-value = 1.208e-09
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.327286 5.520644
## sample estimates:
## ratio of variances
## 3.518524
var.test(PC6~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC6 by house_data$rent
## F = 1.6214, num df = 60, denom df = 138, p-value = 0.02175
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 1.072467 2.544040
## sample estimates:
## ratio of variances
## 1.621417
var.test(PC7~house_data$rent,data=rent_amount_pca)
##
## F test to compare two variances
##
## data: PC7 by house_data$rent
## F = 4.237, num df = 60, denom df = 138, p-value = 2.763e-12
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 2.802515 6.647953
## sample estimates:
## ratio of variances
## 4.237002
# Levene's tests (one-sided)
library(car)
## Loading required package: carData
(LTPC1 <- leveneTest(PC1~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 20.39 1.083e-05 ***
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC1_1sided <- LTPC1[[3]][1]/2)
## [1] 5.41647e-06
(LTPC2 <- leveneTest(PC2~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 10.853 0.001169 **
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC2_1sided=LTPC2[[3]][1]/2)
## [1] 0.0005843576
(LTPC3 <- leveneTest(PC3~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 29.665 1.513e-07 ***
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC3_1sided <- LTPC3[[3]][1]/2)
## [1] 7.56314e-08
(LTPC4 <- leveneTest(PC4~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 12.721 0.0004534 ***
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC4_1sided <- LTPC4[[3]][1]/2)
## [1] 0.0002267165
(LTPC5 <- leveneTest(PC5~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 26.113 7.557e-07 ***
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC5_1sided <- LTPC5[[3]][1]/2)
## [1] 3.778356e-07
(LTPC6 <- leveneTest(PC6~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 8.1872 0.004672 **
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC6_1sided <- LTPC6[[3]][1]/2)
## [1] 0.002335952
(LTPC7 <- leveneTest(PC7~house_data$rent,data=rent_amount_pca))
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 24.227 1.799e-06 ***
## 198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(p_PC7_1sided <- LTPC7[[3]][1]/2)
## [1] 8.994908e-07
# Plotting the scores for the first and second components
plot(rent_amount_pca$PC1, rent_amount_pca$PC2,pch=ifelse(rent_amount_pca$rent == "high",1,5),xlab="PC1", ylab="PC2", main="rent prices against values for PC1 & PC2")
abline(h=0)
abline(v=0)
legend("bottomleft", legend=c("Rent High","Rent Low"), pch=c(1,5))
plot(eigen_rent, xlab = "Component number", ylab = "Component variance", type = "l", main = "Scree diagram")
plot(log(eigen_rent), xlab = "Component number",ylab = "log(Component variance)", type="l",main = "Log(eigenvalue) diagram")
xlim <- range(rent_pca$x[,1])
plot(rent_pca$x,xlim=xlim,ylim=xlim)
rent_pca$rotation[,1]
## area rooms bathroom parking spaces hoa
## 0.4336965 0.3704037 0.4227960 0.3939576 0.3144122
## property tax fire insurance
## 0.2933799 0.3947438
rent_pca$rotation
## PC1 PC2 PC3 PC4 PC5
## area 0.4336965 0.17091900 0.11991336 -0.14262889 -0.35241854
## rooms 0.3704037 0.38742007 0.06788605 0.73084114 0.02399273
## bathroom 0.4227960 0.15626819 0.06607019 0.09708057 0.28600555
## parking spaces 0.3939576 0.08267643 0.27820446 -0.52057449 0.56700711
## hoa 0.3144122 -0.55087277 -0.64250123 0.17481916 0.31296716
## property tax 0.2933799 -0.68199336 0.54402406 0.12426281 -0.30542261
## fire insurance 0.3947438 0.14443506 -0.43643932 -0.34511274 -0.52980928
## PC6 PC7
## area -0.04502695 0.78850847
## rooms 0.39473933 -0.13256958
## bathroom -0.81830269 -0.17780729
## parking spaces 0.39883150 -0.09488344
## hoa 0.08754800 0.22068420
## property tax 0.01872523 -0.20922784
## fire insurance 0.07383965 -0.47705723
#get the original value of the data based on PCA
center <- rent_pca$center
scale <- rent_pca$scale
new_rent <- as.matrix(house_data[,-11])
new_rent
## area rooms bathroom parking spaces animal furniture hoa
## [1,] "120" "3" "4" "3" "acept" "not furnished" "1350"
## [2,] " 45" "1" "1" "1" "not acept" "furnished" "3000"
## [3,] " 50" "2" "1" "1" "acept" "not furnished" " 226"
## [4,] " 35" "1" "1" "0" "acept" "not furnished" " 260"
## [5,] "204" "4" "4" "2" "acept" "not furnished" " 0"
## [6,] "177" "3" "3" "4" "acept" "not furnished" "2700"
## [7,] " 15" "1" "1" "0" "not acept" "not furnished" " 0"
## [8,] " 70" "2" "2" "1" "acept" "furnished" "1800"
## [9,] "180" "3" "3" "2" "acept" "not furnished" " 700"
## [10,] "180" "4" "4" "2" "acept" "not furnished" "2600"
## [11,] " 70" "2" "2" "1" "acept" "not furnished" " 650"
## [12,] "200" "3" "4" "3" "acept" "furnished" "1800"
## [13,] "100" "2" "2" "0" "acept" "not furnished" " 0"
## [14,] " 21" "1" "1" "0" "acept" "not furnished" " 519"
## [15,] " 65" "2" "1" "1" "acept" "not furnished" " 360"
## [16,] "100" "3" "2" "0" "not acept" "not furnished" " 815"
## [17,] " 64" "1" "2" "1" "acept" "furnished" " 650"
## [18,] " 50" "1" "1" "0" "acept" "not furnished" " 985"
## [19,] "137" "3" "3" "1" "acept" "furnished" "1200"
## [20,] " 43" "1" "1" "0" "not acept" "not furnished" " 350"
## [21,] " 49" "2" "1" "1" "acept" "not furnished" " 0"
## [22,] "101" "3" "2" "2" "acept" "furnished" " 541"
## [23,] "330" "4" "5" "4" "acept" "not furnished" " 0"
## [24,] "320" "8" "4" "0" "acept" "not furnished" " 450"
## [25,] " 50" "3" "2" "2" "acept" "not furnished" " 150"
## [26,] " 55" "1" "1" "0" "acept" "not furnished" " 700"
## [27,] " 30" "1" "1" "0" "not acept" "furnished" " 260"
## [28,] " 40" "1" "1" "1" "not acept" "furnished" " 0"
## [29,] " 73" "2" "2" "1" "acept" "not furnished" " 700"
## [30,] "240" "3" "4" "2" "acept" "not furnished" " 1"
## [31,] " 38" "1" "1" "0" "not acept" "not furnished" " 700"
## [32,] "144" "4" "2" "2" "acept" "not furnished" "1820"
## [33,] "161" "3" "3" "3" "acept" "not furnished" "1300"
## [34,] " 85" "2" "1" "1" "acept" "not furnished" " 400"
## [35,] "162" "4" "5" "3" "acept" "not furnished" "1720"
## [36,] "170" "3" "5" "3" "not acept" "not furnished" "2091"
## [37,] "620" "4" "6" "4" "acept" "not furnished" "8133"
## [38,] "120" "3" "2" "1" "acept" "not furnished" " 0"
## [39,] " 23" "1" "1" "0" "not acept" "not furnished" " 550"
## [40,] "135" "4" "2" "1" "acept" "not furnished" " 600"
## [41,] " 48" "1" "1" "0" "not acept" "not furnished" " 0"
## [42,] "209" "4" "4" "4" "acept" "not furnished" "2140"
## [43,] "130" "3" "3" "2" "acept" "not furnished" "1200"
## [44,] " 83" "3" "3" "2" "acept" "furnished" " 450"
## [45,] "190" "4" "3" "1" "acept" "not furnished" "1570"
## [46,] "400" "3" "3" "4" "acept" "not furnished" " 0"
## [47,] "250" "4" "4" "4" "acept" "not furnished" "2600"
## [48,] " 71" "3" "1" "0" "acept" "not furnished" " 320"
## [49,] "303" "3" "4" "4" "acept" "furnished" "4540"
## [50,] " 50" "1" "1" "0" "acept" "furnished" " 200"
## [51,] " 89" "3" "2" "1" "acept" "not furnished" " 860"
## [52,] " 68" "2" "2" "1" "acept" "not furnished" " 400"
## [53,] " 65" "3" "2" "1" "acept" "not furnished" " 512"
## [54,] "305" "3" "3" "3" "acept" "not furnished" "3100"
## [55,] "300" "4" "5" "5" "not acept" "not furnished" "3500"
## [56,] " 63" "2" "1" "0" "acept" "not furnished" " 600"
## [57,] " 35" "1" "1" "0" "acept" "not furnished" " 270"
## [58,] " 50" "1" "1" "0" "not acept" "not furnished" " 0"
## [59,] " 70" "2" "1" "1" "acept" "not furnished" " 729"
## [60,] " 76" "2" "1" "1" "acept" "not furnished" " 0"
## [61,] " 48" "1" "1" "0" "acept" "not furnished" " 309"
## [62,] " 83" "2" "3" "2" "acept" "not furnished" "1555"
## [63,] "100" "3" "3" "0" "acept" "not furnished" " 820"
## [64,] " 40" "1" "1" "0" "not acept" "not furnished" " 0"
## [65,] "170" "3" "3" "2" "acept" "not furnished" " 0"
## [66,] "120" "3" "2" "1" "acept" "not furnished" " 300"
## [67,] "165" "3" "3" "2" "acept" "furnished" "1150"
## [68,] " 44" "1" "1" "1" "acept" "not furnished" " 390"
## [69,] " 22" "1" "1" "0" "not acept" "furnished" " 400"
## [70,] " 44" "1" "1" "0" "acept" "furnished" " 270"
## [71,] " 70" "2" "1" "0" "not acept" "not furnished" " 350"
## [72,] "288" "3" "2" "2" "acept" "not furnished" " 0"
## [73,] "250" "4" "2" "1" "acept" "not furnished" " 0"
## [74,] " 25" "1" "1" "0" "not acept" "not furnished" " 0"
## [75,] " 95" "3" "2" "2" "acept" "furnished" "1000"
## [76,] " 90" "1" "1" "0" "acept" "not furnished" " 0"
## [77,] " 40" "2" "2" "1" "not acept" "not furnished" " 210"
## [78,] " 40" "1" "1" "1" "not acept" "not furnished" " 225"
## [79,] " 51" "1" "1" "0" "not acept" "not furnished" " 429"
## [80,] "150" "4" "2" "2" "acept" "not furnished" "1357"
## [81,] "130" "3" "2" "0" "acept" "not furnished" " 0"
## [82,] " 52" "2" "2" "1" "acept" "not furnished" " 285"
## [83,] "150" "3" "2" "3" "acept" "not furnished" " 0"
## [84,] " 80" "1" "1" "4" "acept" "not furnished" " 0"
## [85,] " 95" "3" "2" "2" "not acept" "not furnished" " 150"
## [86,] " 50" "2" "1" "1" "acept" "not furnished" " 158"
## [87,] "112" "1" "2" "2" "acept" "furnished" " 0"
## [88,] " 15" "1" "1" "0" "acept" "not furnished" " 0"
## [89,] "190" "2" "3" "0" "acept" "not furnished" " 0"
## [90,] "148" "4" "3" "2" "acept" "not furnished" "1886"
## [91,] "250" "4" "2" "2" "acept" "furnished" " 600"
## [92,] "120" "1" "2" "0" "acept" "not furnished" " 0"
## [93,] "550" "3" "5" "8" "acept" "not furnished" " 0"
## [94,] " 40" "1" "1" "0" "not acept" "not furnished" " 450"
## [95,] "137" "3" "3" "1" "acept" "furnished" "1180"
## [96,] " 43" "2" "7" "2" "acept" "not furnished" " 257"
## [97,] "147" "2" "3" "4" "acept" "not furnished" "2600"
## [98,] " 65" "2" "2" "1" "acept" "not furnished" " 532"
## [99,] " 60" "2" "2" "1" "acept" "not furnished" " 440"
## [100,] "100" "1" "2" "0" "not acept" "not furnished" "4320"
## [101,] "362" "4" "5" "0" "not acept" "not furnished" "4138"
## [102,] " 40" "1" "1" "0" "acept" "not furnished" " 928"
## [103,] " 68" "2" "1" "0" "acept" "not furnished" " 593"
## [104,] " 68" "1" "1" "0" "acept" "not furnished" " 500"
## [105,] " 52" "1" "1" "1" "acept" "not furnished" " 470"
## [106,] "268" "4" "4" "4" "acept" "furnished" "3317"
## [107,] "250" "3" "4" "3" "acept" "furnished" "2900"
## [108,] " 39" "2" "1" "0" "acept" "not furnished" " 120"
## [109,] " 20" "1" "1" "0" "acept" "furnished" " 602"
## [110,] " 84" "2" "1" "0" "acept" "not furnished" " 420"
## [111,] " 36" "1" "1" "0" "acept" "furnished" " 345"
## [112,] "387" "4" "6" "6" "acept" "not furnished" "4246"
## [113,] " 30" "1" "1" "1" "acept" "not furnished" " 500"
## [114,] " 80" "1" "1" "1" "acept" "not furnished" " 550"
## [115,] " 72" "2" "3" "0" "acept" "furnished" " 826"
## [116,] "125" "3" "2" "1" "acept" "furnished" "1460"
## [117,] "200" "3" "2" "2" "acept" "not furnished" "2573"
## [118,] " 95" "3" "2" "1" "acept" "not furnished" " 850"
## [119,] "201" "3" "5" "3" "not acept" "furnished" "2550"
## [120,] " 90" "3" "2" "2" "acept" "not furnished" "1300"
## [121,] " 50" "2" "1" "0" "not acept" "not furnished" " 200"
## [122,] " 77" "1" "2" "1" "acept" "furnished" " 750"
## [123,] " 95" "2" "1" "1" "acept" "not furnished" " 300"
## [124,] " 38" "1" "1" "0" "acept" "not furnished" " 0"
## [125,] " 45" "1" "1" "0" "acept" "not furnished" " 300"
## [126,] " 45" "1" "1" "0" "acept" "not furnished" " 50"
## [127,] " 50" "2" "1" "0" "not acept" "not furnished" " 0"
## [128,] " 86" "3" "1" "1" "acept" "not furnished" " 460"
## [129,] " 75" "3" "2" "1" "not acept" "not furnished" " 550"
## [130,] " 45" "1" "1" "1" "acept" "not furnished" " 521"
## [131,] " 30" "1" "1" "0" "acept" "not furnished" " 0"
## [132,] " 90" "2" "1" "0" "acept" "not furnished" " 0"
## [133,] " 65" "3" "1" "1" "acept" "not furnished" " 510"
## [134,] " 45" "1" "1" "1" "not acept" "furnished" "3000"
## [135,] "312" "4" "4" "3" "acept" "not furnished" "4000"
## [136,] " 51" "1" "1" "1" "acept" "furnished" " 714"
## [137,] " 23" "1" "1" "0" "acept" "not furnished" " 399"
## [138,] " 54" "1" "2" "0" "acept" "not furnished" " 245"
## [139,] " 75" "2" "2" "1" "acept" "not furnished" " 700"
## [140,] "170" "4" "3" "3" "acept" "not furnished" "1800"
## [141,] "170" "4" "2" "2" "acept" "not furnished" "1100"
## [142,] " 45" "1" "1" "0" "not acept" "not furnished" " 0"
## [143,] "200" "3" "4" "2" "acept" "not furnished" " 0"
## [144,] " 52" "2" "2" "1" "not acept" "not furnished" " 360"
## [145,] "200" "3" "4" "2" "acept" "not furnished" "1730"
## [146,] "115" "3" "2" "2" "acept" "not furnished" " 0"
## [147,] "360" "5" "4" "4" "acept" "not furnished" " 0"
## [148,] " 80" "1" "1" "0" "acept" "not furnished" " 350"
## [149,] " 64" "2" "2" "2" "acept" "not furnished" " 450"
## [150,] " 27" "1" "1" "0" "not acept" "not furnished" " 0"
## [151,] " 37" "1" "1" "1" "acept" "furnished" " 890"
## [152,] "160" "2" "1" "3" "acept" "not furnished" " 0"
## [153,] " 60" "2" "1" "1" "acept" "not furnished" " 250"
## [154,] " 72" "2" "2" "2" "acept" "not furnished" " 150"
## [155,] " 40" "1" "1" "0" "not acept" "not furnished" " 0"
## [156,] "190" "3" "3" "3" "acept" "not furnished" "3000"
## [157,] " 76" "3" "2" "2" "acept" "not furnished" " 250"
## [158,] "365" "4" "4" "3" "acept" "furnished" "3500"
## [159,] "154" "3" "3" "1" "not acept" "not furnished" "1700"
## [160,] "360" "5" "4" "1" "acept" "not furnished" " 0"
## [161,] " 26" "1" "1" "0" "acept" "not furnished" " 250"
## [162,] " 50" "2" "1" "1" "acept" "not furnished" " 407"
## [163,] " 75" "3" "2" "1" "acept" "not furnished" " 487"
## [164,] "250" "3" "3" "2" "acept" "not furnished" "1900"
## [165,] " 40" "1" "1" "1" "acept" "not furnished" "1840"
## [166,] " 70" "2" "1" "1" "not acept" "not furnished" " 960"
## [167,] "110" "3" "2" "2" "acept" "not furnished" " 380"
## [168,] "470" "4" "6" "3" "acept" "not furnished" " 0"
## [169,] "750" "4" "5" "6" "acept" "not furnished" " 0"
## [170,] " 58" "1" "1" "1" "acept" "not furnished" " 610"
## [171,] "450" "4" "6" "4" "acept" "not furnished" " 0"
## [172,] "187" "2" "3" "3" "acept" "not furnished" "1600"
## [173,] "250" "3" "2" "1" "acept" "not furnished" "3200"
## [174,] " 96" "3" "2" "1" "acept" "not furnished" "1122"
## [175,] " 20" "1" "1" "1" "not acept" "not furnished" " 0"
## [176,] "310" "3" "3" "2" "not acept" "not furnished" " 0"
## [177,] " 25" "1" "1" "0" "acept" "not furnished" " 105"
## [178,] " 38" "1" "1" "0" "not acept" "not furnished" " 457"
## [179,] "117" "3" "2" "1" "acept" "not furnished" " 706"
## [180,] " 64" "1" "1" "0" "acept" "not furnished" " 680"
## [181,] " 89" "3" "3" "0" "acept" "furnished" " 990"
## [182,] "253" "4" "5" "3" "acept" "not furnished" "2100"
## [183,] "213" "4" "3" "5" "acept" "not furnished" " 0"
## [184,] " 90" "3" "2" "1" "acept" "not furnished" "1300"
## [185,] "100" "1" "1" "0" "acept" "furnished" " 0"
## [186,] " 40" "1" "1" "1" "acept" "not furnished" " 0"
## [187,] "285" "4" "4" "4" "acept" "not furnished" "3100"
## [188,] "140" "2" "3" "2" "acept" "furnished" "1000"
## [189,] "128" "4" "2" "0" "not acept" "not furnished" "1100"
## [190,] " 70" "2" "2" "2" "acept" "not furnished" " 610"
## [191,] "157" "3" "3" "1" "acept" "not furnished" "1325"
## [192,] "220" "4" "2" "1" "not acept" "not furnished" "1340"
## [193,] "240" "4" "3" "3" "acept" "not furnished" "2850"
## [194,] "160" "3" "3" "3" "not acept" "not furnished" "1509"
## [195,] " 60" "2" "1" "1" "not acept" "not furnished" " 0"
## [196,] " 60" "2" "1" "1" "acept" "not furnished" " 345"
## [197,] "301" "4" "5" "4" "acept" "furnished" "4265"
## [198,] "105" "3" "2" "1" "not acept" "not furnished" " 500"
## [199,] " 90" "2" "1" "0" "acept" "furnished" " 1"
## [200,] " 55" "2" "1" "1" "acept" "not furnished" " 635"
## rent amount property tax fire insurance
## [1,] " 5600" " 560" " 71"
## [2,] " 5520" " 0" " 70"
## [3,] " 750" " 0" " 10"
## [4,] " 1400" " 0" " 18"
## [5,] " 3440" " 100" " 62"
## [6,] " 6900" " 509" " 89"
## [7,] " 1200" " 0" " 16"
## [8,] " 4200" " 250" " 55"
## [9,] " 2700" " 175" " 40"
## [10,] " 2000" " 584" " 26"
## [11,] " 1000" " 45" " 13"
## [12,] " 5058" " 1200" " 65"
## [13,] " 2500" " 274" " 38"
## [14,] " 1200" " 0" " 16"
## [15,] " 1066" " 15" " 16"
## [16,] " 808" " 81" " 11"
## [17,] " 8900" " 154" " 64"
## [18,] " 2308" " 138" " 30"
## [19,] " 5500" " 303" " 70"
## [20,] " 1080" " 57" " 14"
## [21,] " 1000" " 0" " 13"
## [22,] " 4200" " 142" " 62"
## [23,] " 6500" " 750" " 98"
## [24,] "10500" " 350" "158"
## [25,] " 2800" " 30" " 43"
## [26,] " 1020" " 29" " 15"
## [27,] " 1490" " 15" " 22"
## [28,] " 970" " 0" " 13"
## [29,] " 1250" " 150" " 16"
## [30,] " 8200" " 625" "124"
## [31,] " 1650" " 0" " 25"
## [32,] " 4800" " 667" " 61"
## [33,] " 4500" " 640" " 58"
## [34,] " 1600" " 0" " 21"
## [35,] " 3700" " 525" " 47"
## [36,] " 4500" " 951" " 19"
## [37,] "15000" " 4520" "191"
## [38,] " 1020" " 25" " 15"
## [39,] " 1500" " 0" " 20"
## [40,] " 2200" " 138" " 30"
## [41,] " 1150" " 34" " 18"
## [42,] " 7300" " 587" " 98"
## [43,] " 3500" " 342" " 45"
## [44,] " 3655" " 142" " 49"
## [45,] " 3500" " 506" " 46"
## [46,] " 4250" " 160" " 64"
## [47,] "15000" " 1250" "191"
## [48,] " 1250" " 46" " 19"
## [49,] "18000" " 0" "229"
## [50,] " 1300" " 12" " 19"
## [51,] " 1800" " 70" " 23"
## [52,] " 1800" " 82" " 27"
## [53,] " 1860" " 135" " 24"
## [54,] " 5500" " 2167" " 70"
## [55,] " 1990" "10830" " 26"
## [56,] " 1110" " 0" " 15"
## [57,] " 1300" " 0" " 17"
## [58,] " 1200" " 0" " 19"
## [59,] " 900" " 122" " 12"
## [60,] " 2000" " 45" " 31"
## [61,] " 700" " 28" " 11"
## [62,] " 3200" " 735" " 41"
## [63,] " 6330" " 148" " 81"
## [64,] " 2040" " 70" " 31"
## [65,] " 8000" " 209" "143"
## [66,] " 2100" " 149" " 28"
## [67,] " 2710" " 84" " 35"
## [68,] " 1200" " 74" " 16"
## [69,] " 1956" " 50" " 25"
## [70,] " 550" " 6" " 9"
## [71,] " 1050" " 116" " 14"
## [72,] " 3700" " 457" " 56"
## [73,] " 2700" " 209" " 48"
## [74,] " 1200" " 35" " 16"
## [75,] " 4500" " 325" " 58"
## [76,] " 2200" " 109" " 34"
## [77,] " 900" " 0" " 12"
## [78,] " 1080" " 65" " 14"
## [79,] " 3250" " 37" " 42"
## [80,] " 3700" " 211" " 50"
## [81,] " 1400" " 10" " 22"
## [82,] " 900" " 59" " 12"
## [83,] " 2430" " 38" " 32"
## [84,] " 1000" " 75" " 16"
## [85,] " 1650" " 145" " 22"
## [86,] " 1140" " 0" " 16"
## [87,] " 8389" " 0" "107"
## [88,] " 700" " 0" " 10"
## [89,] " 2500" " 14" " 41"
## [90,] " 5500" " 399" " 74"
## [91,] " 3500" " 0" " 47"
## [92,] " 761" " 129" " 10"
## [93,] "10000" " 3825" "151"
## [94,] " 2390" " 114" " 31"
## [95,] " 2900" " 214" " 38"
## [96,] " 2270" " 0" " 29"
## [97,] " 1200" " 375" " 16"
## [98,] " 1820" " 49" " 24"
## [99,] " 1250" " 38" " 16"
## [100,] " 3100" " 0" " 40"
## [101,] "15000" " 1975" "200"
## [102,] " 1950" " 61" " 26"
## [103,] " 1000" " 400" " 13"
## [104,] " 1500" " 0" " 20"
## [105,] " 2000" " 89" " 26"
## [106,] " 6000" " 899" " 77"
## [107,] " 9800" " 125" "125"
## [108,] " 1450" " 43" " 19"
## [109,] " 1800" " 130" " 23"
## [110,] " 1700" " 85" " 22"
## [111,] " 1600" " 30" " 21"
## [112,] " 6647" " 2235" " 85"
## [113,] " 1560" " 61" " 20"
## [114,] " 2800" " 100" " 36"
## [115,] " 2400" " 220" " 32"
## [116,] " 3500" " 295" " 46"
## [117,] "10000" " 1121" "127"
## [118,] " 2300" " 0" " 30"
## [119,] "10000" " 795" "127"
## [120,] " 3500" " 250" " 45"
## [121,] " 950" " 0" " 13"
## [122,] " 7740" " 209" " 99"
## [123,] " 2800" " 69" " 41"
## [124,] " 1680" " 0" " 22"
## [125,] " 800" " 108" " 11"
## [126,] " 640" " 84" " 10"
## [127,] " 750" " 59" " 13"
## [128,] " 3150" " 0" " 40"
## [129,] " 2125" " 67" " 27"
## [130,] " 600" " 38" " 8"
## [131,] " 1000" " 50" " 13"
## [132,] " 1199" " 42" " 19"
## [133,] " 1800" " 0" " 23"
## [134,] " 5520" " 0" " 70"
## [135,] "15000" " 1084" "194"
## [136,] " 4650" " 190" " 59"
## [137,] " 2100" " 42" " 27"
## [138,] " 720" " 46" " 10"
## [139,] " 3160" " 165" " 41"
## [140,] " 6000" " 881" " 80"
## [141,] " 4000" " 297" " 54"
## [142,] " 950" " 30" " 15"
## [143,] " 3250" " 292" " 49"
## [144,] " 1650" " 86" " 22"
## [145,] " 5000" " 306" " 65"
## [146,] " 3000" " 71" " 50"
## [147,] " 9000" " 600" "148"
## [148,] " 1000" " 0" " 14"
## [149,] " 2800" " 30" " 36"
## [150,] " 835" " 0" " 13"
## [151,] " 4150" " 110" " 53"
## [152,] " 4000" " 0" " 61"
## [153,] " 1300" " 30" " 18"
## [154,] " 3700" " 184" " 47"
## [155,] " 880" " 48" " 14"
## [156,] "13000" " 84" "165"
## [157,] " 2100" " 0" " 31"
## [158,] " 8800" " 459" "118"
## [159,] " 6500" " 0" " 83"
## [160,] " 4600" " 158" " 76"
## [161,] " 800" " 12" " 12"
## [162,] " 1715" " 12" " 22"
## [163,] " 1280" " 109" " 18"
## [164,] " 9500" " 500" "121"
## [165,] " 2990" " 409" " 38"
## [166,] " 2600" " 0" " 33"
## [167,] " 2150" " 192" " 29"
## [168,] " 7200" " 1134" "109"
## [169,] "13000" " 1500" "196"
## [170,] " 1600" " 89" " 21"
## [171,] "11600" " 1042" "175"
## [172,] " 3100" " 292" " 40"
## [173,] " 8500" " 5000" "108"
## [174,] " 3050" " 231" " 39"
## [175,] " 1000" " 0" " 14"
## [176,] "10400" " 0" "157"
## [177,] " 1330" " 0" " 17"
## [178,] " 1050" " 0" " 14"
## [179,] " 3400" " 217" " 50"
## [180,] " 1100" " 50" " 14"
## [181,] " 3550" " 84" " 45"
## [182,] " 4100" " 0" " 52"
## [183,] " 3900" " 298" " 59"
## [184,] " 2050" " 230" " 27"
## [185,] " 2720" " 138" " 41"
## [186,] " 1200" " 0" " 19"
## [187,] "15000" " 973" "191"
## [188,] " 3000" " 113" " 44"
## [189,] " 2000" " 10" " 26"
## [190,] " 2100" " 240" " 28"
## [191,] " 4500" " 347" " 58"
## [192,] " 6000" " 17" " 77"
## [193,] "12000" " 700" "153"
## [194,] " 5850" " 528" " 88"
## [195,] " 1150" " 50" " 19"
## [196,] " 820" " 16" " 11"
## [197,] "12500" " 1600" "159"
## [198,] " 1200" " 135" " 16"
## [199,] " 4000" " 1" " 52"
## [200,] " 1990" " 80" " 26"
#drop(scale(new_rent,center=center, scale=scale)%*%rent_pca$rotation[,1])
predict(rent_pca)[,1]
## [1] 1.56783825 -0.57639521 -1.50346548 -2.07514390 1.34530122 2.24975917
## [7] -2.23846999 -0.22912144 0.64873235 1.79476843 -0.95873902 2.13362330
## [13] -1.01660046 -2.07539668 -1.35466295 -0.76938889 -0.82924339 -1.67879982
## [19] 0.63656545 -2.03744269 -1.54280272 0.16777452 3.19501820 3.50284784
## [25] -0.32699280 -1.89708178 -2.05561487 -1.89149015 -0.87692112 1.85262375
## [31] -1.88522590 1.13964853 1.30826933 -1.22973070 2.24230187 1.94733013
## [37] 8.72831120 -0.61880179 -2.02535867 0.07631034 -2.08583746 2.94958392
## [43] 0.68623055 0.27919326 1.10879850 2.05071676 4.21959089 -1.27087429
## [49] 4.58113442 -2.02223866 -0.42267359 -0.90365748 -0.58003387 2.90035484
## [55] 6.70167586 -1.58788243 -2.08095556 -2.07987411 -1.23803192 -1.27346638
## [61] -2.06371912 0.37233762 0.16309312 -1.99481328 1.30865679 -0.39000716
## [67] 0.64357804 -1.72324081 -2.01219554 -2.11306394 -1.60270788 0.77543632
## [73] 0.52578996 -2.19000223 0.28960911 -1.76754748 -1.21365160 -1.80275583
## [79] -1.75325429 0.80748929 -0.80315118 -1.13014395 0.19883740 -0.85903456
## [85] -0.30037935 -1.47075763 -0.22483293 -2.28955542 -0.41144395 1.51846473
## [91] 0.89449869 -1.53674605 6.19640421 -1.86005657 0.33212522 0.80830540
## [97] 1.13265608 -0.91478591 -1.03005630 -0.22835744 4.55935064 -1.78927235
## [103] -1.46833723 -1.86780325 -1.58158590 3.40634597 2.81079495 -1.76194430
## [109] -1.95834325 -1.47171259 -2.01386835 5.76211882 -1.71655905 -1.36509520
## [115] -0.65189695 0.13935881 1.93908906 -0.36387251 3.06217521 0.21859561
## [121] -1.76243407 -0.43837393 -1.02785008 -2.09994961 -2.05366442 -2.13691077
## [127] -1.79886574 -0.73346935 -0.52651398 -1.76290055 -2.19205769 -1.60079651
## [133] -0.94453039 -0.57639521 4.53213015 -1.20831130 -1.99402495 -1.74622046
## [139] -0.65198241 2.05137269 0.87380363 -2.12397932 0.96227605 -1.01666875
## [145] 1.57023222 -0.04858875 3.68932809 -1.91380925 -0.56676741 -2.21839448
## [151] -1.28894407 -0.15718144 -1.38189083 -0.47778490 -2.14612555 2.62293011
## [157] -0.31225768 3.76474618 0.85651631 2.11184808 -2.15956186 -1.34879407
## [163] -0.60762535 2.02586190 -1.05921271 -1.03323781 -0.10756112 3.97320720
## [169] 6.40500435 -1.56351179 4.70912592 0.91652794 3.01780491 -0.14094557
## [175] -1.95900629 1.89765204 -2.16356341 -2.04455537 -0.08406563 -1.87051599
## [181] -0.15840553 2.57672105 1.93043286 -0.21811819 -1.66127191 -1.84040471
## [187] 4.40504874 0.27881638 -0.16493909 -0.50611546 0.65734764 0.96346403
## [193] 3.16873531 1.58293083 -1.43496833 -1.41999697 5.01064356 -0.49933014
## [199] -1.33180077 -1.21380272
#The aboved two gives us the same thing. predict is a good function to know.
house_data$rent <- as.factor(house_data$rent)
out <- sapply(1:5, function(i){plot(house_data$rent,rent_pca$x[,i],xlab=paste("PC",i,sep=""),ylab="Rent Prices")})
pairs(rent_pca$x[,1:5], ylim = c(-6,4),xlim = c(-6,4),panel=function(x,y,...){text(x,y,house_data$rent)})
